A survey on algorithms for mining frequent itemsets over data streams

The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend learning has led to the study of online mining of frequent itemsets (FIs). Unlike mining static databases, mining data streams poses many new challenges. In addition to the one-scan nature, the unbounded memory requirement and the high data arrival rate of data streams, the combinatorial explosion of itemsets exacerbates the mining task. The high complexity of the FI mining problem hinders the application of the stream mining techniques. We recognize that a critical review of existing techniques is needed in order to design and develop efficient mining algorithms and data structures that are able to match the processing rate of the mining with the high arrival rate of data streams. Within a unifying set of notations and terminologies, we describe in this paper the efforts and main techniques for mining data streams and present a comprehensive survey of a number of the state-of-the-art algorithms on mining frequent itemsets over data streams. We classify the stream-mining techniques into two categories based on the window model that they adopt in order to provide insights into how and why the techniques are useful. Then, we further analyze the algorithms according to whether they are exact or approximate and, for approximate approaches, whether they are false-positive or false-negative. We also discuss various interesting issues, including the merits and limitations in existing research and substantive areas for future research.     

A computer program for estimating the re-transformed mean in heteroscedastic two-part models

The population of health care costs is typically skewed, heteroscedastic, and may include zero costs. Without proper accounting for these special distributional features, resulting prediction may be biased, and wrong inferences about the distribution of patients’ health care costs may be made. Welsh and Zhou [A.H. Welsh, X.H. Zhou, Estimating the retransformed mean in a heteroscedastic two-part model, J. Stat. Plan. Inference 136 (2006) 860–881] proposed a semi-parametric regression model, which addressed these special features. In this paper we developed a software program to implement this statistical method, which would provide better prediction of health care costs for clinical researchers.

Our program computed two mean estimators, their asymptotical standard deviation, confidence interval, and optional bootstrap confidence interval. Our program included user-friendly interactive mode and more efficient and flexible batch mode. It was written in free statistical computing language R and could be run on a wide variety of platforms.     

基于DSP2812的静止无功补偿控制器

针对电力系统中无功补偿装置发展的现状,介绍了一种基于DSP2812的静止无功补偿装置控制器的设计.该控制器充分利用了DSP2812内部资源丰富的特点,即响应速度快、动态性能好,系统结构简单、可靠性高、成本低.试验结果表明,该控制器具有优异的性能,可用于低压系统的动态无功补偿.     

基于数字图像处理的控制系统动态性能测量

针对目前位置控制伺服系统动态性能较难准确测量的问题,提出了一种基于激光和数字图像处理的测量思路,实现了对运动轨迹的数字化监测,并以某火炮检修中的位置控制伺服系统为例,详细介绍了系统组成、软件处理系统及相应的试验结果.这是开展位置控制伺服系统动态性能准确测量的有益尝试.     

基于FAT32的数据恢复系统的设计

恶意删除或数据误删现象时常发生,数据恢复的成败关系到信息的安全.根据基于FAT32系统的删除文件的四种情形,设计基于FAT32的数据恢复算法.系统依据被删除文件的创建时间与该分区或该分区下某一目录中现存文件的创建时间的比较结果来定位被删除文件,读取该文件信息,恢复被删除文件.系统具有简单、易用、灵活、高效等特点.     

平均曲率运动的三种实现方案的比较研究

讨论了曲率运动演化方程的三种数值方案:离散中值滤波方案、显式水平集方案和半隐式水平集方案的原理与算法,并对它们的效率、精度以及旋转不变性等性能进行了对比实验研究.结果表明,半隐式加性算子分裂(AOS)算法相对于另两种方案性能上明显占优.     

校园网业务系统数据整合设计与研究

校园网业务系统数据整合目的在于使得业务系统数据的利用率达到最佳,消除由于异构数据源引起的信息孤岛。它对基于Java语言的Hibernate框架进行业务系统的数据整合技术进行了分析和研究,并与现有的结构进行了对比。通过对现有的整合平台和整合技术的对比,从而结合Java语言自身的优势和特点提出基于Hibernate框架结构的整合技术,并实现了数据整合的目的。     

J2EE集成开源框架的应用与研究

在设计模式MVC思想的基础之上,该文提出了基于WebWork、Spring和Hibernate的J2EE轻量级集成框架的应用。首先简要的介绍了这三种开源软件的基础技术,然后给出了整个集成框架的原理,最后分析了在Web项目中集成框架的应用,并说明了集成框架在代码复用和开发效率上的优势。